Overview

Dataset statistics

Number of variables20
Number of observations33638
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.4 MiB
Average record size in memory168.0 B

Variable types

Categorical4
DateTime1
Numeric15

Alerts

channelTitle has a high cardinality: 2266 distinct valuesHigh cardinality
comment_count has 793 (2.4%) zerosZeros
log_channel_avg_n_comments_7D has 727 (2.2%) zerosZeros

Reproduction

Analysis started2023-04-08 17:43:32.332938
Analysis finished2023-04-08 17:43:51.249329
Duration18.92 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

channelTitle
Categorical

Distinct2266
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size525.6 KiB
Sky Sports Premier League
 
354
The United Stand
 
297
MoreSidemen
 
236
BT Sport
 
225
WWE
 
182
Other values (2261)
32344 

Length

Max length49
Median length37
Mean length12.564481
Min length2

Characters and Unicode

Total characters422644
Distinct characters170
Distinct categories12 ?
Distinct scripts8 ?
Distinct blocks9 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)< 0.1%

Sample

1st rowImAllexx
2nd rowStephen Tries Less
3rd rowCold Ones
4th rowDimension 20
5th rowImAllexx

Common Values

ValueCountFrequency (%)
Sky Sports Premier League 354
 
1.1%
The United Stand 297
 
0.9%
MoreSidemen 236
 
0.7%
BT Sport 225
 
0.7%
WWE 182
 
0.5%
Sidemen 154
 
0.5%
FORMULA 1 148
 
0.4%
Beta Squad 142
 
0.4%
BBC Sport 138
 
0.4%
Sky Sports Football 133
 
0.4%
Other values (2256) 31629
94.0%

Length

2023-04-08T19:43:51.309940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 2192
 
3.3%
sports 953
 
1.4%
sky 748
 
1.1%
627
 
1.0%
sport 623
 
0.9%
league 529
 
0.8%
united 423
 
0.6%
football 401
 
0.6%
boxing 381
 
0.6%
premier 376
 
0.6%
Other values (2930) 58727
89.0%

Most occurring characters

ValueCountFrequency (%)
e 35096
 
8.3%
32458
 
7.7%
a 28698
 
6.8%
i 24001
 
5.7%
o 23195
 
5.5%
r 21339
 
5.0%
n 21008
 
5.0%
t 19372
 
4.6%
s 16459
 
3.9%
l 15395
 
3.6%
Other values (160) 185623
43.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 289842
68.6%
Uppercase Letter 95092
 
22.5%
Space Separator 32458
 
7.7%
Decimal Number 2410
 
0.6%
Other Punctuation 1428
 
0.3%
Dash Punctuation 703
 
0.2%
Other Letter 499
 
0.1%
Open Punctuation 69
 
< 0.1%
Close Punctuation 69
 
< 0.1%
Math Symbol 57
 
< 0.1%
Other values (2) 17
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
32
 
6.4%
29
 
5.8%
19
 
3.8%
19
 
3.8%
19
 
3.8%
13
 
2.6%
13
 
2.6%
13
 
2.6%
11
 
2.2%
11
 
2.2%
Other values (55) 320
64.1%
Lowercase Letter
ValueCountFrequency (%)
e 35096
12.1%
a 28698
 
9.9%
i 24001
 
8.3%
o 23195
 
8.0%
r 21339
 
7.4%
n 21008
 
7.2%
t 19372
 
6.7%
s 16459
 
5.7%
l 15395
 
5.3%
h 9973
 
3.4%
Other values (36) 75306
26.0%
Uppercase Letter
ValueCountFrequency (%)
S 10106
 
10.6%
T 8536
 
9.0%
M 6267
 
6.6%
B 5911
 
6.2%
C 5812
 
6.1%
A 5072
 
5.3%
E 4927
 
5.2%
L 4333
 
4.6%
F 4149
 
4.4%
V 4119
 
4.3%
Other values (19) 35860
37.7%
Decimal Number
ValueCountFrequency (%)
2 602
25.0%
1 570
23.7%
7 255
10.6%
0 241
10.0%
3 216
 
9.0%
5 169
 
7.0%
4 154
 
6.4%
8 78
 
3.2%
6 75
 
3.1%
9 50
 
2.1%
Other Punctuation
ValueCountFrequency (%)
' 506
35.4%
. 359
25.1%
& 216
15.1%
: 138
 
9.7%
! 112
 
7.8%
/ 43
 
3.0%
, 31
 
2.2%
? 18
 
1.3%
5
 
0.4%
Dash Punctuation
ValueCountFrequency (%)
- 640
91.0%
63
 
9.0%
Open Punctuation
ValueCountFrequency (%)
( 64
92.8%
[ 5
 
7.2%
Close Punctuation
ValueCountFrequency (%)
) 64
92.8%
] 5
 
7.2%
Math Symbol
ValueCountFrequency (%)
+ 45
78.9%
| 12
 
21.1%
Space Separator
ValueCountFrequency (%)
32458
100.0%
Final Punctuation
ValueCountFrequency (%)
13
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 384768
91.0%
Common 37211
 
8.8%
Hangul 311
 
0.1%
Cyrillic 166
 
< 0.1%
Katakana 70
 
< 0.1%
Han 55
 
< 0.1%
Arabic 33
 
< 0.1%
Hiragana 30
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 35096
 
9.1%
a 28698
 
7.5%
i 24001
 
6.2%
o 23195
 
6.0%
r 21339
 
5.5%
n 21008
 
5.5%
t 19372
 
5.0%
s 16459
 
4.3%
l 15395
 
4.0%
S 10106
 
2.6%
Other values (47) 170099
44.2%
Common
ValueCountFrequency (%)
32458
87.2%
- 640
 
1.7%
2 602
 
1.6%
1 570
 
1.5%
' 506
 
1.4%
. 359
 
1.0%
7 255
 
0.7%
0 241
 
0.6%
3 216
 
0.6%
& 216
 
0.6%
Other values (20) 1148
 
3.1%
Hangul
ValueCountFrequency (%)
32
 
10.3%
29
 
9.3%
19
 
6.1%
19
 
6.1%
19
 
6.1%
13
 
4.2%
13
 
4.2%
13
 
4.2%
11
 
3.5%
11
 
3.5%
Other values (17) 132
42.4%
Cyrillic
ValueCountFrequency (%)
а 25
15.1%
н 25
15.1%
р 14
 
8.4%
к 11
 
6.6%
е 11
 
6.6%
д 8
 
4.8%
Є 7
 
4.2%
в 7
 
4.2%
б 7
 
4.2%
ч 7
 
4.2%
Other values (8) 44
26.5%
Katakana
ValueCountFrequency (%)
5
 
7.1%
5
 
7.1%
5
 
7.1%
5
 
7.1%
5
 
7.1%
5
 
7.1%
5
 
7.1%
5
 
7.1%
5
 
7.1%
5
 
7.1%
Other values (4) 20
28.6%
Han
ValueCountFrequency (%)
5
9.1%
5
9.1%
5
9.1%
5
9.1%
5
9.1%
5
9.1%
5
9.1%
5
9.1%
5
9.1%
5
9.1%
Arabic
ValueCountFrequency (%)
ا 9
27.3%
ة 6
18.2%
ق 3
 
9.1%
ن 3
 
9.1%
ل 3
 
9.1%
ر 3
 
9.1%
ب 3
 
9.1%
ع 3
 
9.1%
Hiragana
ValueCountFrequency (%)
6
20.0%
6
20.0%
6
20.0%
6
20.0%
6
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 421767
99.8%
Hangul 311
 
0.1%
Cyrillic 166
 
< 0.1%
None 131
 
< 0.1%
Punctuation 81
 
< 0.1%
Katakana 70
 
< 0.1%
CJK 55
 
< 0.1%
Arabic 33
 
< 0.1%
Hiragana 30
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 35096
 
8.3%
32458
 
7.7%
a 28698
 
6.8%
i 24001
 
5.7%
o 23195
 
5.5%
r 21339
 
5.1%
n 21008
 
5.0%
t 19372
 
4.6%
s 16459
 
3.9%
l 15395
 
3.7%
Other values (69) 184746
43.8%
None
ValueCountFrequency (%)
é 64
48.9%
ø 54
41.2%
å 5
 
3.8%
ü 4
 
3.1%
Ż 4
 
3.1%
Punctuation
ValueCountFrequency (%)
63
77.8%
13
 
16.0%
5
 
6.2%
Hangul
ValueCountFrequency (%)
32
 
10.3%
29
 
9.3%
19
 
6.1%
19
 
6.1%
19
 
6.1%
13
 
4.2%
13
 
4.2%
13
 
4.2%
11
 
3.5%
11
 
3.5%
Other values (17) 132
42.4%
Cyrillic
ValueCountFrequency (%)
а 25
15.1%
н 25
15.1%
р 14
 
8.4%
к 11
 
6.6%
е 11
 
6.6%
д 8
 
4.8%
Є 7
 
4.2%
в 7
 
4.2%
б 7
 
4.2%
ч 7
 
4.2%
Other values (8) 44
26.5%
Arabic
ValueCountFrequency (%)
ا 9
27.3%
ة 6
18.2%
ق 3
 
9.1%
ن 3
 
9.1%
ل 3
 
9.1%
ر 3
 
9.1%
ب 3
 
9.1%
ع 3
 
9.1%
Hiragana
ValueCountFrequency (%)
6
20.0%
6
20.0%
6
20.0%
6
20.0%
6
20.0%
Katakana
ValueCountFrequency (%)
5
 
7.1%
5
 
7.1%
5
 
7.1%
5
 
7.1%
5
 
7.1%
5
 
7.1%
5
 
7.1%
5
 
7.1%
5
 
7.1%
5
 
7.1%
Other values (4) 20
28.6%
CJK
ValueCountFrequency (%)
5
9.1%
5
9.1%
5
9.1%
5
9.1%
5
9.1%
5
9.1%
5
9.1%
5
9.1%
5
9.1%
5
9.1%
Distinct5949
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Memory size525.6 KiB
Minimum2022-10-10 17:47:41+00:00
Maximum2023-03-31 04:00:14+00:00
2023-04-08T19:43:51.400133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:51.488672image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

likes
Real number (ℝ)

Distinct27785
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80288.18
Minimum0
Maximum8173676
Zeros107
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size525.6 KiB
2023-04-08T19:43:51.584572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2026.7
Q110051
median26616.5
Q367196
95-th percentile256884.95
Maximum8173676
Range8173676
Interquartile range (IQR)57145

Descriptive statistics

Standard deviation263596.07
Coefficient of variation (CV)3.2831242
Kurtosis254.05159
Mean80288.18
Median Absolute Deviation (MAD)20237
Skewness13.124639
Sum2.7007338 × 109
Variance6.9482889 × 1010
MonotonicityNot monotonic
2023-04-08T19:43:51.676132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 107
 
0.3%
4467 6
 
< 0.1%
7105 6
 
< 0.1%
5196 6
 
< 0.1%
2330 5
 
< 0.1%
1224 5
 
< 0.1%
4656 5
 
< 0.1%
3439 5
 
< 0.1%
747 5
 
< 0.1%
5895 5
 
< 0.1%
Other values (27775) 33483
99.5%
ValueCountFrequency (%)
0 107
0.3%
16 1
 
< 0.1%
20 1
 
< 0.1%
21 5
 
< 0.1%
103 1
 
< 0.1%
115 1
 
< 0.1%
116 3
 
< 0.1%
117 1
 
< 0.1%
118 1
 
< 0.1%
170 1
 
< 0.1%
ValueCountFrequency (%)
8173676 1
< 0.1%
7981980 1
< 0.1%
7753404 1
< 0.1%
7445757 1
< 0.1%
7374871 1
< 0.1%
7116719 1
< 0.1%
7026323 1
< 0.1%
6857566 1
< 0.1%
6559801 1
< 0.1%
6409807 1
< 0.1%

comment_count
Real number (ℝ)

Distinct10035
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5042.0347
Minimum0
Maximum518733
Zeros793
Zeros (%)2.4%
Negative0
Negative (%)0.0%
Memory size525.6 KiB
2023-04-08T19:43:51.924975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile140
Q1684
median1716
Q34030
95-th percentile15118.3
Maximum518733
Range518733
Interquartile range (IQR)3346

Descriptive statistics

Standard deviation18719.09
Coefficient of variation (CV)3.7126063
Kurtosis305.79201
Mean5042.0347
Median Absolute Deviation (MAD)1267.5
Skewness15.252035
Sum1.6960396 × 108
Variance3.5040432 × 108
MonotonicityNot monotonic
2023-04-08T19:43:52.014527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 793
 
2.4%
411 25
 
0.1%
492 25
 
0.1%
170 24
 
0.1%
208 24
 
0.1%
377 24
 
0.1%
277 23
 
0.1%
173 23
 
0.1%
137 23
 
0.1%
133 22
 
0.1%
Other values (10025) 32632
97.0%
ValueCountFrequency (%)
0 793
2.4%
1 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 3
 
< 0.1%
8 2
 
< 0.1%
9 1
 
< 0.1%
11 3
 
< 0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
518733 1
< 0.1%
512804 1
< 0.1%
506126 1
< 0.1%
497019 1
< 0.1%
484342 1
< 0.1%
483168 1
< 0.1%
478221 1
< 0.1%
468972 1
< 0.1%
467790 1
< 0.1%
460937 1
< 0.1%

category
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size525.6 KiB
Entertainment
7137 
Sports
6264 
Gaming
6140 
Music
4081 
People & Blogs
2958 
Other values (9)
7058 

Length

Max length20
Median length16
Mean length9.5686426
Min length5

Characters and Unicode

Total characters321870
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowComedy
2nd rowComedy
3rd rowComedy
4th rowComedy
5th rowComedy

Common Values

ValueCountFrequency (%)
Entertainment 7137
21.2%
Sports 6264
18.6%
Gaming 6140
18.3%
Music 4081
12.1%
People & Blogs 2958
8.8%
Comedy 1488
 
4.4%
Autos & Vehicles 1030
 
3.1%
Science & Technology 944
 
2.8%
Film & Animation 890
 
2.6%
Education 830
 
2.5%
Other values (4) 1876
 
5.6%

Length

2023-04-08T19:43:52.098038image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7698
15.7%
entertainment 7137
14.6%
sports 6264
12.8%
gaming 6140
12.5%
music 4081
8.3%
people 2958
 
6.0%
blogs 2958
 
6.0%
comedy 1488
 
3.0%
autos 1030
 
2.1%
vehicles 1030
 
2.1%
Other values (13) 8250
16.8%

Most occurring characters

ValueCountFrequency (%)
t 32880
 
10.2%
n 32570
 
10.1%
e 28835
 
9.0%
i 24516
 
7.6%
o 20240
 
6.3%
s 17568
 
5.5%
m 16677
 
5.2%
a 15518
 
4.8%
15396
 
4.8%
r 13790
 
4.3%
Other values (24) 103880
32.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 257440
80.0%
Uppercase Letter 41336
 
12.8%
Space Separator 15396
 
4.8%
Other Punctuation 7698
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 32880
12.8%
n 32570
12.7%
e 28835
11.2%
i 24516
9.5%
o 20240
7.9%
s 17568
 
6.8%
m 16677
 
6.5%
a 15518
 
6.0%
r 13790
 
5.4%
l 10656
 
4.1%
Other values (9) 44190
17.2%
Uppercase Letter
ValueCountFrequency (%)
E 8356
20.2%
S 7787
18.8%
G 6140
14.9%
M 4081
9.9%
P 3866
9.4%
B 2958
 
7.2%
A 2052
 
5.0%
C 1488
 
3.6%
T 1333
 
3.2%
V 1030
 
2.5%
Other values (3) 2245
 
5.4%
Space Separator
ValueCountFrequency (%)
15396
100.0%
Other Punctuation
ValueCountFrequency (%)
& 7698
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 298776
92.8%
Common 23094
 
7.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 32880
 
11.0%
n 32570
 
10.9%
e 28835
 
9.7%
i 24516
 
8.2%
o 20240
 
6.8%
s 17568
 
5.9%
m 16677
 
5.6%
a 15518
 
5.2%
r 13790
 
4.6%
l 10656
 
3.6%
Other values (22) 85526
28.6%
Common
ValueCountFrequency (%)
15396
66.7%
& 7698
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 321870
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 32880
 
10.2%
n 32570
 
10.1%
e 28835
 
9.0%
i 24516
 
7.6%
o 20240
 
6.3%
s 17568
 
5.5%
m 16677
 
5.2%
a 15518
 
4.8%
15396
 
4.8%
r 13790
 
4.3%
Other values (24) 103880
32.3%

is_weekday
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size525.6 KiB
1
24178 
0
9460 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33638
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 24178
71.9%
0 9460
 
28.1%

Length

2023-04-08T19:43:52.171627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-08T19:43:52.248719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
1 24178
71.9%
0 9460
 
28.1%

Most occurring characters

ValueCountFrequency (%)
1 24178
71.9%
0 9460
 
28.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33638
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 24178
71.9%
0 9460
 
28.1%

Most occurring scripts

ValueCountFrequency (%)
Common 33638
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 24178
71.9%
0 9460
 
28.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33638
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 24178
71.9%
0 9460
 
28.1%

year
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size525.6 KiB
2023
17146 
2022
16492 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters134552
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2023 17146
51.0%
2022 16492
49.0%

Length

2023-04-08T19:43:52.305952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-08T19:43:52.375295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2023 17146
51.0%
2022 16492
49.0%

Most occurring characters

ValueCountFrequency (%)
2 83768
62.3%
0 33638
25.0%
3 17146
 
12.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 134552
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 83768
62.3%
0 33638
25.0%
3 17146
 
12.7%

Most occurring scripts

ValueCountFrequency (%)
Common 134552
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 83768
62.3%
0 33638
25.0%
3 17146
 
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 134552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 83768
62.3%
0 33638
25.0%
3 17146
 
12.7%

month
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4549319
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size525.6 KiB
2023-04-08T19:43:52.428331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q311
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)9

Descriptive statistics

Standard deviation4.6472729
Coefficient of variation (CV)0.71995692
Kurtosis-1.8848835
Mean6.4549319
Median Absolute Deviation (MAD)2
Skewness0.03381348
Sum217131
Variance21.597145
MonotonicityNot monotonic
2023-04-08T19:43:52.484652image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
12 6282
18.7%
1 6035
17.9%
11 5937
17.6%
2 5658
16.8%
3 5453
16.2%
10 4273
12.7%
ValueCountFrequency (%)
1 6035
17.9%
2 5658
16.8%
3 5453
16.2%
10 4273
12.7%
11 5937
17.6%
12 6282
18.7%
ValueCountFrequency (%)
12 6282
18.7%
11 5937
17.6%
10 4273
12.7%
3 5453
16.2%
2 5658
16.8%
1 6035
17.9%

category_likes_sum_7D
Real number (ℝ)

Distinct1606
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16481574
Minimum15643
Maximum90682766
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size525.6 KiB
2023-04-08T19:43:52.565954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum15643
5-th percentile803536
Q14604558
median11111019
Q323866021
95-th percentile51771419
Maximum90682766
Range90667123
Interquartile range (IQR)19261463

Descriptive statistics

Standard deviation16116768
Coefficient of variation (CV)0.97786586
Kurtosis2.5661916
Mean16481574
Median Absolute Deviation (MAD)7881185
Skewness1.5932844
Sum5.5440718 × 1011
Variance2.5975022 × 1014
MonotonicityNot monotonic
2023-04-08T19:43:52.651565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32687834 138
 
0.4%
23905881 114
 
0.3%
6335859 112
 
0.3%
69482545 110
 
0.3%
24092706 104
 
0.3%
8480678 99
 
0.3%
26571820 97
 
0.3%
21562508 95
 
0.3%
24062554 93
 
0.3%
5908280 93
 
0.3%
Other values (1596) 32583
96.9%
ValueCountFrequency (%)
15643 12
< 0.1%
26080 5
 
< 0.1%
27133 6
 
< 0.1%
43903 5
 
< 0.1%
52478 10
< 0.1%
59292 7
 
< 0.1%
61050 6
 
< 0.1%
63046 5
 
< 0.1%
76043 21
0.1%
76400 5
 
< 0.1%
ValueCountFrequency (%)
90682766 35
0.1%
87474008 6
 
< 0.1%
87159638 3
 
< 0.1%
83269744 61
0.2%
82432895 25
0.1%
77191229 20
 
0.1%
75668927 32
0.1%
74143804 60
0.2%
73331667 12
 
< 0.1%
70785152 30
0.1%
Distinct1606
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6598474 × 108
Minimum731370
Maximum1.6148627 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size525.6 KiB
2023-04-08T19:43:52.735927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum731370
5-th percentile22049056
Q11.4363415 × 108
median2.9601022 × 108
Q35.188142 × 108
95-th percentile9.4907536 × 108
Maximum1.6148627 × 109
Range1.6141314 × 109
Interquartile range (IQR)3.7518006 × 108

Descriptive statistics

Standard deviation2.9561989 × 108
Coefficient of variation (CV)0.8077383
Kurtosis1.0769837
Mean3.6598474 × 108
Median Absolute Deviation (MAD)1.7906161 × 108
Skewness1.1367142
Sum1.2310995 × 1013
Variance8.7391118 × 1016
MonotonicityNot monotonic
2023-04-08T19:43:52.825155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
700014588 138
 
0.4%
534303455 114
 
0.3%
332264949 112
 
0.3%
888725941 110
 
0.3%
551566098 104
 
0.3%
420596825 99
 
0.3%
629095157 97
 
0.3%
381163689 95
 
0.3%
461135747 93
 
0.3%
297406307 93
 
0.3%
Other values (1596) 32583
96.9%
ValueCountFrequency (%)
731370 7
< 0.1%
1051431 12
< 0.1%
1587628 5
< 0.1%
1754195 5
< 0.1%
1898683 5
< 0.1%
2056205 5
< 0.1%
2068058 10
< 0.1%
2203691 12
< 0.1%
2299354 6
< 0.1%
2586291 5
< 0.1%
ValueCountFrequency (%)
1614862746 20
 
0.1%
1570069508 60
0.2%
1476847124 39
0.1%
1390928784 10
 
< 0.1%
1383919734 37
0.1%
1348165503 51
0.2%
1325954439 5
 
< 0.1%
1313810826 5
 
< 0.1%
1309594834 30
0.1%
1307536509 28
0.1%
Distinct1598
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1009241.2
Minimum655
Maximum6646543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size525.6 KiB
2023-04-08T19:43:52.920897image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum655
5-th percentile68350
Q1341390
median678068
Q31348006
95-th percentile2970243.7
Maximum6646543
Range6645888
Interquartile range (IQR)1006616

Descriptive statistics

Standard deviation1019151.1
Coefficient of variation (CV)1.0098192
Kurtosis7.2821275
Mean1009241.2
Median Absolute Deviation (MAD)446178
Skewness2.3113389
Sum3.3948855 × 1010
Variance1.0386689 × 1012
MonotonicityNot monotonic
2023-04-08T19:43:53.007320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1903765 138
 
0.4%
1599197 114
 
0.3%
612763 112
 
0.3%
6484139 110
 
0.3%
1479976 104
 
0.3%
757885 99
 
0.3%
1616698 97
 
0.3%
1633700 95
 
0.3%
2082675 93
 
0.3%
628494 93
 
0.3%
Other values (1588) 32583
96.9%
ValueCountFrequency (%)
655 12
< 0.1%
1501 5
< 0.1%
3761 7
< 0.1%
4619 5
< 0.1%
4857 6
< 0.1%
4898 6
< 0.1%
5376 10
< 0.1%
5914 5
< 0.1%
6341 6
< 0.1%
7701 5
< 0.1%
ValueCountFrequency (%)
6646543 12
 
< 0.1%
6558678 35
 
0.1%
6484139 110
0.3%
6374569 5
 
< 0.1%
6269194 25
 
0.1%
6212018 6
 
< 0.1%
6198508 3
 
< 0.1%
6007095 61
0.2%
5838715 18
 
0.1%
5727134 30
 
0.1%
Distinct357
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean205.94908
Minimum5
Maximum504
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size525.6 KiB
2023-04-08T19:43:53.094518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile29
Q1119
median223
Q3290
95-th percentile348
Maximum504
Range499
Interquartile range (IQR)171

Descriptive statistics

Standard deviation107.76966
Coefficient of variation (CV)0.52328304
Kurtosis-0.75630968
Mean205.94908
Median Absolute Deviation (MAD)78
Skewness-0.16341531
Sum6927715
Variance11614.299
MonotonicityNot monotonic
2023-04-08T19:43:53.184087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
317 640
 
1.9%
283 369
 
1.1%
286 347
 
1.0%
293 307
 
0.9%
311 291
 
0.9%
288 282
 
0.8%
306 275
 
0.8%
301 271
 
0.8%
261 269
 
0.8%
274 244
 
0.7%
Other values (347) 30343
90.2%
ValueCountFrequency (%)
5 45
0.1%
6 31
0.1%
7 37
0.1%
8 8
 
< 0.1%
9 11
 
< 0.1%
10 10
 
< 0.1%
11 67
0.2%
12 55
0.2%
13 31
0.1%
14 42
0.1%
ValueCountFrequency (%)
504 83
0.2%
499 80
0.2%
466 80
0.2%
462 31
 
0.1%
455 138
0.4%
445 90
0.3%
440 19
 
0.1%
433 39
 
0.1%
429 30
 
0.1%
415 63
0.2%

category_avg_n_views_7D
Real number (ℝ)

Distinct1606
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1763143.1
Minimum87619.25
Maximum9164009.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size525.6 KiB
2023-04-08T19:43:53.275393image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum87619.25
5-th percentile600253.39
Q1964059.05
median1309035.7
Q32162178.7
95-th percentile4381006.5
Maximum9164009.2
Range9076389.9
Interquartile range (IQR)1198119.7

Descriptive statistics

Standard deviation1267167.9
Coefficient of variation (CV)0.7186983
Kurtosis5.4202306
Mean1763143.1
Median Absolute Deviation (MAD)469560.32
Skewness2.0526088
Sum5.9308607 × 1010
Variance1.6057146 × 1012
MonotonicityNot monotonic
2023-04-08T19:43:53.358585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1538493.6 138
 
0.4%
1539779.409 114
 
0.3%
1028684.053 112
 
0.3%
3734142.609 110
 
0.3%
1802503.588 104
 
0.3%
1155485.783 99
 
0.3%
2279330.279 97
 
0.3%
1483127.195 95
 
0.3%
2794762.103 93
 
0.3%
864553.218 93
 
0.3%
Other values (1596) 32583
96.9%
ValueCountFrequency (%)
87619.25 12
< 0.1%
104481.4286 7
< 0.1%
111687.2353 5
< 0.1%
129628.8824 12
< 0.1%
137080.3333 5
< 0.1%
146182.9167 5
< 0.1%
147718.4286 10
< 0.1%
195111.2973 5
< 0.1%
204789.3125 12
< 0.1%
225804.8095 6
< 0.1%
ValueCountFrequency (%)
9164009.158 7
 
< 0.1%
8979632.617 9
 
< 0.1%
8722608.378 60
0.2%
8162605.857 28
0.1%
8149681.604 22
 
0.1%
8074313.73 20
 
0.1%
8060189.117 5
 
< 0.1%
8001217.296 6
 
< 0.1%
7754119.526 5
 
< 0.1%
7736514.875 5
 
< 0.1%
Distinct1606
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5041.7073
Minimum54.583333
Maximum35276.114
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size525.6 KiB
2023-04-08T19:43:53.444901image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum54.583333
5-th percentile1219.1895
Q12361.4333
median3494.2632
Q35547.4763
95-th percentile15740.893
Maximum35276.114
Range35221.531
Interquartile range (IQR)3186.043

Descriptive statistics

Standard deviation4835.2961
Coefficient of variation (CV)0.95905926
Kurtosis8.1093555
Mean5041.7073
Median Absolute Deviation (MAD)1424.9283
Skewness2.6931422
Sum1.6959295 × 108
Variance23380088
MonotonicityNot monotonic
2023-04-08T19:43:53.529968image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4184.098901 138
 
0.4%
4608.636888 114
 
0.3%
1897.099071 112
 
0.3%
27244.28151 110
 
0.3%
4836.522876 104
 
0.3%
2082.101648 99
 
0.3%
5857.601449 97
 
0.3%
6356.809339 95
 
0.3%
12622.27273 93
 
0.3%
1827.017442 93
 
0.3%
Other values (1596) 32583
96.9%
ValueCountFrequency (%)
54.58333333 12
 
< 0.1%
88.29411765 5
 
< 0.1%
384 10
 
< 0.1%
394.2666667 5
 
< 0.1%
418.4782609 6
 
< 0.1%
445.2727273 6
 
< 0.1%
537.2857143 7
 
< 0.1%
588.8684211 21
 
0.1%
592.862963 48
0.1%
624.9458333 78
0.2%
ValueCountFrequency (%)
35276.11429 1
 
< 0.1%
31625.04082 3
 
< 0.1%
31077.675 9
 
< 0.1%
30601.07389 6
 
< 0.1%
30432.98058 25
0.1%
30085.6789 35
0.1%
28917.18812 22
0.1%
28350.33835 28
0.1%
28074.18627 30
0.1%
28044.48523 12
 
< 0.1%
Distinct1606
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.051713
Minimum8.8122127
Maximum395.67239
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size525.6 KiB
2023-04-08T19:43:53.623399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum8.8122127
5-th percentile14.102707
Q118.754188
median22.328325
Q331.96496
95-th percentile55.536422
Maximum395.67239
Range386.86017
Interquartile range (IQR)13.210772

Descriptive statistics

Standard deviation16.530738
Coefficient of variation (CV)0.58929514
Kurtosis57.500152
Mean28.051713
Median Absolute Deviation (MAD)4.6464095
Skewness4.4856553
Sum943603.53
Variance273.26531
MonotonicityNot monotonic
2023-04-08T19:43:53.712542image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.41514143 138
 
0.4%
22.35029343 114
 
0.3%
52.44197338 112
 
0.3%
12.79063599 110
 
0.3%
22.89348893 104
 
0.3%
49.59471696 99
 
0.3%
23.67527542 97
 
0.3%
17.67715003 95
 
0.3%
19.16403999 93
 
0.3%
50.33720592 93
 
0.3%
Other values (1596) 32583
96.9%
ValueCountFrequency (%)
8.812212714 7
 
< 0.1%
8.962186619 28
0.1%
9.203613064 34
0.1%
9.764872605 15
< 0.1%
9.772804038 14
< 0.1%
9.817020806 10
 
< 0.1%
10.09641748 1
 
< 0.1%
10.1396995 7
 
< 0.1%
10.14014852 3
 
< 0.1%
10.17710327 3
 
< 0.1%
ValueCountFrequency (%)
395.6723865 6
< 0.1%
247.9343087 2
 
< 0.1%
218.2694187 12
< 0.1%
153.3788298 10
< 0.1%
151.8580327 6
< 0.1%
149.7723671 4
 
< 0.1%
149.4290223 8
< 0.1%
147.0681675 5
< 0.1%
137.3814237 9
< 0.1%
134.4366452 7
< 0.1%
Distinct33
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7364588
Minimum1
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size525.6 KiB
2023-04-08T19:43:53.798613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q15
median6
Q38
95-th percentile17
Maximum34
Range33
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.237595
Coefficient of variation (CV)0.54774349
Kurtosis7.1204724
Mean7.7364588
Median Absolute Deviation (MAD)1
Skewness2.437969
Sum260239
Variance17.957211
MonotonicityNot monotonic
2023-04-08T19:43:53.871540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
6 9012
26.8%
5 7078
21.0%
7 5157
15.3%
4 1943
 
5.8%
8 1870
 
5.6%
11 1325
 
3.9%
10 1275
 
3.8%
9 1080
 
3.2%
12 905
 
2.7%
14 549
 
1.6%
Other values (23) 3444
 
10.2%
ValueCountFrequency (%)
1 59
 
0.2%
2 86
 
0.3%
3 351
 
1.0%
4 1943
 
5.8%
5 7078
21.0%
6 9012
26.8%
7 5157
15.3%
8 1870
 
5.6%
9 1080
 
3.2%
10 1275
 
3.8%
ValueCountFrequency (%)
34 7
 
< 0.1%
33 12
 
< 0.1%
32 16
 
< 0.1%
30 59
0.2%
29 36
 
0.1%
28 38
 
0.1%
27 97
0.3%
26 115
0.3%
25 76
0.2%
24 55
0.2%
Distinct5728
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.535197
Minimum10.301941
Maximum18.468713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size525.6 KiB
2023-04-08T19:43:53.960631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10.301941
5-th percentile11.615125
Q112.725731
median13.510059
Q314.23208
95-th percentile15.547145
Maximum18.468713
Range8.1667719
Interquartile range (IQR)1.5063493

Descriptive statistics

Standard deviation1.1828047
Coefficient of variation (CV)0.087387328
Kurtosis0.38579638
Mean13.535197
Median Absolute Deviation (MAD)0.75250059
Skewness0.33853105
Sum455296.96
Variance1.399027
MonotonicityNot monotonic
2023-04-08T19:43:54.048682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.72237944 30
 
0.1%
14.50349223 21
 
0.1%
13.87006616 21
 
0.1%
14.70974986 21
 
0.1%
13.70258549 20
 
0.1%
14.91179392 20
 
0.1%
13.76109105 19
 
0.1%
12.5641469 19
 
0.1%
13.07833706 18
 
0.1%
15.17938678 18
 
0.1%
Other values (5718) 33431
99.4%
ValueCountFrequency (%)
10.30194147 5
< 0.1%
10.33259106 5
< 0.1%
10.35018346 4
< 0.1%
10.41619141 5
< 0.1%
10.51484154 6
< 0.1%
10.56052169 4
< 0.1%
10.58380908 4
< 0.1%
10.62196129 3
< 0.1%
10.64902555 6
< 0.1%
10.65968465 5
< 0.1%
ValueCountFrequency (%)
18.46871339 8
< 0.1%
18.29898045 6
< 0.1%
18.04791982 6
< 0.1%
17.97317417 5
< 0.1%
17.89133124 7
< 0.1%
17.84951909 10
< 0.1%
17.82907087 12
< 0.1%
17.68155746 7
< 0.1%
17.66205633 7
< 0.1%
17.62879505 7
< 0.1%
Distinct5420
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3483248
Minimum0
Maximum12.877826
Zeros727
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size525.6 KiB
2023-04-08T19:43:54.139888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.020114
Q16.5977984
median7.4675615
Q38.3141124
95-th percentile9.6130238
Maximum12.877826
Range12.877826
Interquartile range (IQR)1.7163141

Descriptive statistics

Standard deviation1.7020868
Coefficient of variation (CV)0.23162923
Kurtosis5.8468793
Mean7.3483248
Median Absolute Deviation (MAD)0.85686576
Skewness-1.5014114
Sum247182.95
Variance2.8970996
MonotonicityNot monotonic
2023-04-08T19:43:54.232301image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 727
 
2.2%
8.959620053 30
 
0.1%
5.690359454 28
 
0.1%
5.493061443 22
 
0.1%
7.921881139 21
 
0.1%
7.286843818 21
 
0.1%
7.458186157 21
 
0.1%
8.159592649 21
 
0.1%
8.396493406 20
 
0.1%
7.075182513 20
 
0.1%
Other values (5410) 32707
97.2%
ValueCountFrequency (%)
0 727
2.2%
0.7884573604 5
 
< 0.1%
2.131627295 7
 
< 0.1%
2.41293315 6
 
< 0.1%
3.120895417 6
 
< 0.1%
3.252586882 7
 
< 0.1%
3.285198468 7
 
< 0.1%
3.583518938 5
 
< 0.1%
3.779633817 5
 
< 0.1%
3.799227511 3
 
< 0.1%
ValueCountFrequency (%)
12.87782593 6
< 0.1%
12.87484452 8
< 0.1%
12.70182016 7
< 0.1%
12.62925752 8
< 0.1%
12.47987373 8
< 0.1%
12.46051846 6
< 0.1%
12.38980293 1
 
< 0.1%
12.27883879 5
< 0.1%
12.23040609 6
< 0.1%
12.22482264 8
< 0.1%
Distinct5714
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite101
Infinite (%)0.3%
Meaninf
Minimum1.6367736
Maximuminf
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size525.6 KiB
2023-04-08T19:43:54.325073image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1.6367736
5-th percentile2.4275282
Q12.8844136
median3.2495608
Q33.7921768
95-th percentile4.7773719
Maximuminf
Rangeinf
Interquartile range (IQR)0.90776314

Descriptive statistics

Standard deviationnan
Coefficient of variation (CV)nan
Kurtosisnan
Meaninf
Median Absolute Deviation (MAD)0.42707329
Skewnessnan
Suminf
Variancenan
MonotonicityNot monotonic
2023-04-08T19:43:54.412335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
inf 101
 
0.3%
2.256324831 30
 
0.1%
4.453739331 21
 
0.1%
3.74195992 21
 
0.1%
3.849949457 21
 
0.1%
4.312732525 20
 
0.1%
3.864527765 20
 
0.1%
4.474059683 19
 
0.1%
5.302909104 19
 
0.1%
4.445918514 18
 
0.1%
Other values (5704) 33348
99.1%
ValueCountFrequency (%)
1.63677365 5
< 0.1%
1.699826496 6
< 0.1%
1.747560838 4
< 0.1%
1.750238762 3
< 0.1%
1.813505405 1
 
< 0.1%
1.835234424 7
< 0.1%
1.862092812 6
< 0.1%
1.866960856 1
 
< 0.1%
1.880434055 5
< 0.1%
1.881974346 6
< 0.1%
ValueCountFrequency (%)
inf 101
0.3%
12.3137195 7
 
< 0.1%
9.945829187 6
 
< 0.1%
9.572999645 6
 
< 0.1%
7.852680152 3
 
< 0.1%
7.77538346 7
 
< 0.1%
7.479748605 8
 
< 0.1%
7.363494653 5
 
< 0.1%
7.077303912 7
 
< 0.1%
6.960501187 6
 
< 0.1%

log_view_count
Real number (ℝ)

Distinct33334
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.499555
Minimum10.184221
Maximum18.831095
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size525.6 KiB
2023-04-08T19:43:54.500727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum10.184221
5-th percentile11.567827
Q112.677655
median13.463461
Q314.238559
95-th percentile15.546679
Maximum18.831095
Range8.6468748
Interquartile range (IQR)1.5609033

Descriptive statistics

Standard deviation1.2098555
Coefficient of variation (CV)0.089621876
Kurtosis0.41564972
Mean13.499555
Median Absolute Deviation (MAD)0.78014436
Skewness0.35697165
Sum454098.04
Variance1.4637503
MonotonicityNot monotonic
2023-04-08T19:43:54.583841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.91889045 2
 
< 0.1%
12.69119269 2
 
< 0.1%
12.7159338 2
 
< 0.1%
12.66625319 2
 
< 0.1%
12.39793191 2
 
< 0.1%
14.05036922 2
 
< 0.1%
12.13969777 2
 
< 0.1%
12.36797463 2
 
< 0.1%
12.18495626 2
 
< 0.1%
11.94721772 2
 
< 0.1%
Other values (33324) 33618
99.9%
ValueCountFrequency (%)
10.18422054 1
< 0.1%
10.20307349 1
< 0.1%
10.23005374 1
< 0.1%
10.24331131 1
< 0.1%
10.26444332 1
< 0.1%
10.26475693 1
< 0.1%
10.27839036 1
< 0.1%
10.28595694 1
< 0.1%
10.28864791 1
< 0.1%
10.30229723 1
< 0.1%
ValueCountFrequency (%)
18.83109532 1
< 0.1%
18.77552485 1
< 0.1%
18.70952901 1
< 0.1%
18.6310795 1
< 0.1%
18.60368259 1
< 0.1%
18.52109564 1
< 0.1%
18.51248618 1
< 0.1%
18.41454322 1
< 0.1%
18.30581274 1
< 0.1%
18.2759113 1
< 0.1%

Interactions

2023-04-08T19:43:49.686290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:32.878971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:34.059285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:35.208615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:36.353401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:37.671975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:38.892167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:41.205730image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:42.333130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:43.535567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:46.086995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:47.292875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:48.475289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:49.764384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:32.969190image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:34.138930image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:35.291055image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:36.433271image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:37.758263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:38.972977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:41.283549image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:42.416599image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:43.619637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:46.172461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:47.373621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:48.579502image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:49.835910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:33.042224image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:34.208414image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:35.364689image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:36.517368image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:37.837607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:39.047659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:41.356126image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:42.492547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:43.697135image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:46.248095image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:47.446589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:48.671576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:49.905382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:33.115147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:34.280747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:35.434744image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:36.600032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:37.914495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:39.121391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:41.426294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:42.567972image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:43.772911image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:46.325533image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:47.518479image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:48.752217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:49.975626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:33.187603image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:34.350547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:35.507035image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:36.672152image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:37.991155image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:39.194164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:41.496310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:42.642536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:43.848715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:46.400116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:47.590900image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:48.825263image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:50.056281image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:33.273112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:34.432112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:35.590107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:36.756659image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:38.077313image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:39.278277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:41.578607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:42.730409image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:43.935515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:46.488604image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:47.673846image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:48.908971image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:50.203025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:33.427679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:34.582816image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:35.744848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:36.915006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:38.237961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:39.430671image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:41.729246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:42.889093image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:44.095729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:46.646809image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:47.828049image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:49.060825image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:50.272509image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:33.502018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:34.660274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:35.817307image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:36.982952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:38.313981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:39.504014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:41.796624image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:42.962872image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:44.171975image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:46.722932image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:47.908286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:49.132332image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:50.350498image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:33.585274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:34.738684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:35.896388image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:37.063464image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:38.399596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:39.584535image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:41.875766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:43.044169image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:44.255449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:46.806796image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:48.004304image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:49.211380image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:50.506686image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:33.749772image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:34.904131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:36.057857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:37.223648image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:38.569395image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:39.748480image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:42.033540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:43.214166image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:44.422326image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:46.975836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:48.169015image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:49.385452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:50.585318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:33.833364image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:34.986075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:36.140495image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:37.305180image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:38.656343image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:39.831841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:42.114953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:43.301316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:44.509490image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:47.060630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:48.252934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:49.467310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:50.659953image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:33.911421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:35.061355image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:36.213927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:37.381297image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:38.737981image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:39.908619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:42.191361image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:43.385048image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:44.590286image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:47.140956image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:48.329006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:49.543109image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:50.731112image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:33.987527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:35.136682image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:36.284881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:37.453489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:38.816518image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:39.984014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:42.263625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:43.462481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:44.668799image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:47.218884image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:48.404164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-08T19:43:49.617235image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Missing values

2023-04-08T19:43:50.850824image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-08T19:43:51.090026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

channelTitlepublishedAtlikescomment_countcategoryis_weekdayyearmonthcategory_likes_sum_7Dcategory_view_count_sum_7Dcategory_comment_count_sum_7Dcategory_trending_videos_last_7Dcategory_avg_n_views_7Dcategory_avg_n_comments_7Dcategory_view_like_ratio_7Dchannel_trending_videos_last_7Dlog_channel_avg_n_views_7Dlog_channel_avg_n_comments_7Dlog_channel_view_like_ratio_7Dlog_view_count
35ImAllexx2022-10-10 20:26:38+00:0010103242Comedy12022109776343.0130991700.0777977.065.02.015257e+0611968.87692313.3988456.012.0728515.5379902.77208411.860790
36Stephen Tries Less2022-10-10 17:47:41+00:008920202Comedy12022109776343.0130991700.0777977.065.02.015257e+0611968.87692313.3988455.011.7145695.3687762.58584711.549566
45Cold Ones2022-10-11 19:00:02+00:00881632162Comedy12022103943247.076300805.0325881.071.01.074659e+064589.87323919.3497406.014.1694147.8437832.70261413.923978
46Dimension 202022-10-12 17:00:09+00:00184111615Comedy12022103862875.074009869.0325095.062.01.193708e+065243.46774219.1592714.012.3715807.4058002.51536912.149793
47ImAllexx2022-10-10 20:26:38+00:0011089243Comedy12022109776343.0130991700.0777977.065.02.015257e+0611968.87692313.3988456.012.0728515.5379902.77208411.996968
48TryPods2022-10-11 15:00:23+00:0015330821Comedy12022103943247.076300805.0325881.071.01.074659e+064589.87323919.3497404.012.9947746.7205223.33433612.924339
49Stephen Tries Less2022-10-10 17:47:41+00:009704213Comedy12022109776343.0130991700.0777977.065.02.015257e+0611968.87692313.3988455.011.7145695.3687762.58584711.673666
54Cold Ones2022-10-11 19:00:02+00:00964462597Comedy12022103943247.076300805.0325881.071.01.074659e+064589.87323919.3497406.014.1694147.8437832.70261414.064473
55BlueJay2022-10-11 15:00:43+00:00308081145Comedy12022103943247.076300805.0325881.071.01.074659e+064589.87323919.3497404.012.9319427.1246802.54406812.709871
56ImAllexx2022-10-10 20:26:38+00:0011698246Comedy12022109776343.0130991700.0777977.065.02.015257e+0611968.87692313.3988456.012.0728515.5379902.77208412.067799
channelTitlepublishedAtlikescomment_countcategoryis_weekdayyearmonthcategory_likes_sum_7Dcategory_view_count_sum_7Dcategory_comment_count_sum_7Dcategory_trending_videos_last_7Dcategory_avg_n_views_7Dcategory_avg_n_comments_7Dcategory_view_like_ratio_7Dchannel_trending_videos_last_7Dlog_channel_avg_n_views_7Dlog_channel_avg_n_comments_7Dlog_channel_view_like_ratio_7Dlog_view_count
35011Tyler, The Creator2023-03-29 14:00:37+00:0040538715406Autos & Vehicles1202332921797.037010728.0120356.039.0948993.0256413086.05128212.6671115.014.8629789.4085842.37958515.348825
35012Simone Giertz2023-03-30 16:00:34+00:00448072370Autos & Vehicles1202332405722.029555771.0108348.036.0820993.6388893009.66666712.2856141.013.2388187.7710672.60543813.238816
35013Diesel Creek2023-03-30 12:05:31+00:00400141979Autos & Vehicles1202332405722.029555771.0108348.036.0820993.6388893009.66666712.2856141.012.7705717.5908522.28133312.770568
35014DRIVETRIBE2023-03-29 17:00:47+00:0013441833Autos & Vehicles1202332921797.037010728.0120356.039.0948993.0256413086.05128212.6671111.012.5820816.7262333.12112312.582078
35015High Peak Autos2023-03-29 18:00:05+00:0063451168Autos & Vehicles1202332921797.037010728.0120356.039.0948993.0256413086.05128212.6671111.011.6594047.0639042.95732911.659395
35016Tyler, The Creator2023-03-27 14:02:13+00:0033220112955Autos & Vehicles1202332359738.034486132.0106685.048.0718461.0833332222.60416714.6143903.014.6835399.3131682.34584715.144441
35017Hoonigan2023-03-27 16:00:23+00:00239771476Autos & Vehicles1202332359738.034486132.0106685.048.0718461.0833332222.60416714.6143904.013.1018287.3206923.16007313.250626
35018The Late Brake Show2023-03-26 16:02:08+00:00116531090Autos & Vehicles0202331784735.031236309.087456.047.0664602.3191491860.76595717.5019314.012.3325626.9295173.09515212.484889
35019Mat Armstrong2023-03-26 17:00:32+00:001093344206Autos & Vehicles0202331784735.031236309.087456.047.0664602.3191491860.76595717.50193112.014.1264698.0699682.72612814.371996
35020Car Throttle2023-03-24 18:30:25+00:009810338Autos & Vehicles1202331642483.037316479.0132639.045.0829255.0888892947.53333322.7195536.012.2617185.8522023.17769212.355600